Sensitivity index for conservation priority ranking in the oil spill response: A case study for the coastal and marine species and habitat types in the Baltic Sea.

Numerous anthropogenic stressors, such as habitat alteration and nutrient enrichment, affect coastal and marine ecosystems around the globe. An additional threat to these ecosystems is accidental oil pollution. The proactive planning of efficient oil spill response actions requires a firm understanding of the spatiotemporal distribution of ecological coastal values at stake, and how these values can be protected in case of an oil spill. In this paper, literature and expert knowledge regarding the life history attributes of coastal and marine species were used to build a sensitivity index to assess the differences in the potential of species and habitat types to be safeguarded from oil. The developed index prioritizes sensitive species and habitat types based on 1) their conservation value, 2) the oil-induced loss and recovery potential, and 3) the effectiveness of oil retention booms and protection sheets to safeguard these entities. The final sensitivity index compares the predicted difference in the state of populations and habitat types five years after an oil spill with and without protective actions. The higher the difference, the more worthwhile the management actions are. Hence, compared to other oil spill sensitivity and vulnerability indexes presented in the literature, the developed index considers the usefulness of protective measures explicitly. We apply the developed index to a case study area in the Northern Baltic Sea to demonstrate the approach. It is noteworthy that the developed index is applicable to other areas as well, as the approach is based on the biological attributes of species and habitat types instead of individual occurrences.

A multi-criteria decision analysis model for ship biofouling management in the Baltic Sea.

Biofouling of ship hulls form a vector for the introduction of non-indigenous organisms worldwide. Through increasing friction, the organisms attached to ships' hulls increase the fuel consumption, leading to both higher fuel costs and air emissions. At the same time, ship biofouling management causes both ecological risks and monetary costs. All these aspects should be considered case-specifically in the search of sustainable management strategies. Applying Bayesian networks, we developed a multi-criteria decision analysis model to compare biofouling management strategies in the Baltic Sea, given the characteristics of a ship, its operating profile and operational environment, considering the comprehensive environmental impact and the monetary costs. The model is demonstrated for three scenarios (SC1-3) and sub-scenarios (A-C), comparing the alternative biofouling management strategies in relation to NIS (non-indigenous species) introduction risk, eco-toxicological risk due to biocidal coating, carbon dioxide emissions and costs related to fuel consumption, in-water cleaning and hull coating. The scenarios demonstrate that by the careful consideration of the hull fouling management strategy, both money and environment can be saved. We suggest biocidal-free coating with a regular in-water cleaning using a capture system is generally the lowest-risk option. The best biocidal-free coating type and the optimal in-water cleaning interval should be evaluated case-specifically, though. In some cases, however, biocidal coating remains a justifiable option.

Developing a conceptual influence diagram for socio-eco-technical systems analysis of biofouling management in shipping - A Baltic Sea case study.

Ship hulls create a vector for the transportation of harmful non-indigenous species (NIS) all over the world. To sustainably prevent NIS introductions, the joint consideration of environmental, economic and social aspects in the search of optimal biofouling management strategies is needed. This article presents a multi-perspective soft systems analysis of the biofouling management problem, based on an extensive literature review and expert knowledge collected in the Baltic Sea area during 2018-2020. The resulting conceptual influence diagram (CID) reveals the multidimensionality of the problem by visualizing the causal relations between the key elements and demonstrating the entanglement of social, ecological and technical aspects. Seen as a boundary object, we suggest the CID can support open dialogue and better risk communication among stakeholders by providing an illustrative and directly applicable starting point for the discussions. It also provides a basis for quantitative management optimization in the future.

Causal Approach to Determining the Environmental Risks of Seabed Mining.

Mineral deposits containing commercially exploitable metals are of interest for seabed mineral extraction in both the deep sea and shallow sea areas. However, the development of seafloor mining is underpinned by high uncertainties on the implementation of the activities and their consequences for the environment. To avoid unbridled expansion of maritime activities, the environmental risks of new types of activities should be carefully evaluated prior to permitting them, yet observational data on the impacts is mostly missing. Here, we examine the environmental risks of seabed mining using a causal, probabilistic network approach. Drawing on a series of expert interviews, we outline the cause-effect pathways related to seabed mining activities to inform quantitative risk assessments. The approach consists of (1) iterative model building with experts to identify the causal connections between seabed mining activities and the affected ecosystem components and (2) quantitative probabilistic modeling. We demonstrate the approach in the Baltic Sea, where seabed mining been has tested and the ecosystem is well studied. The model is used to provide estimates of mortality of benthic fauna under alternative mining scenarios, offering a quantitative means to highlight the uncertainties around the impacts of mining. We further outline requirements for operationalizing quantitative risk assessments in data-poor cases, highlighting the importance of a predictive approach to risk identification. The model can be used to support permitting processes by providing a more comprehensive description of the potential environmental impacts of seabed resource use, allowing iterative updating of the model as new information becomes available.

Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions.

The risk of a large-scale oil spill remains significant in marine environments as international maritime transport continues to grow. The environmental as well as the socio-economic impacts of a large-scale oil spill could be substantial. Oil spill models and modeling tools for Pollution Preparedness and Response (PPR) can support effective risk management. However, there is a lack of integrated approaches that consider oil spill risks comprehensively, learn from all information sources, and treat the system uncertainties in an explicit manner. Recently, the use of the international ISO 31000:2018 risk management framework has been suggested as a suitable basis for supporting oil spill PPR risk management. Bayesian networks (BNs) are graphical models that express uncertainty in a probabilistic form and can thus support decision-making processes when risks are complex and data are scarce. While BNs have increasingly been used for oil spill risk assessment (OSRA) for PPR, no link between the BNs literature and the ISO 31000:2018 framework has previously been made. This study explores how Bayesian risk models can be aligned with the ISO 31000:2018 framework by offering a flexible approach to integrate various sources of probabilistic knowledge. In order to gain insight in the current utilization of BNs for oil spill risk assessment and management (OSRA-BNs) for maritime oil spill preparedness and response, a literature review was performed. The review focused on articles presenting BN models that analyze the occurrence of oil spills, consequence mitigation in terms of offshore and shoreline oil spill response, and impacts of spills on the variables of interest. Based on the results, the study discusses the benefits of applying BNs to the ISO 31000:2018 framework as well as the challenges and further research needs.

"This Is What We Don't Know": Treating Epistemic Uncertainty in Bayesian Networks for Risk Assessment.

Failing to communicate current knowledge limitations, that is, epistemic uncertainty, in environmental risk assessment (ERA) may have severe consequences for decision making. Bayesian networks (BNs) have gained popularity in ERA, primarily because they can combine variables from different models and integrate data and expert judgment. This paper highlights potential gaps in the treatment of uncertainty when using BNs for ERA and proposes a consistent framework (and a set of methods) for treating epistemic uncertainty to help close these gaps. The proposed framework describes the treatment of epistemic uncertainty about the model structure, parameters, expert judgment, data, management scenarios, and the assessment's output. We identify issues related to the differentiation between aleatory and epistemic uncertainty and the importance of communicating both uncertainties associated with the assessment predictions (direct uncertainty) and the strength of knowledge supporting the assessment (indirect uncertainty). Probabilities, intervals, or scenarios are expressions of direct epistemic uncertainty. The type of BN determines the treatment of parameter uncertainty: epistemic, aleatory, or predictive. Epistemic BNs are useful for probabilistic reasoning about states of the world in light of evidence. Aleatory BNs are the most relevant for ERA, but they are not sufficient to treat epistemic uncertainty alone because they do not explicitly express parameter uncertainty. For uncertainty analysis, we recommend embedding an aleatory BN into a model for parameter uncertainty. Bayesian networks do not contain information about uncertainty in the model structure, which requires several models. Statistical models (e.g., hierarchical modeling outside the BNs) are required to consider uncertainties and variability associated with data. We highlight the importance of being open about things one does not know and carefully choosing a method to precisely communicate both direct and indirect uncertainty in ERA. Integr Environ Assess Manag 2021;17:221-232. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Impacts of Oil Spills on Arctic Marine Ecosystems: A Quantitative and Probabilistic Risk Assessment Perspective.

Oil spills resulting from maritime accidents pose a poorly understood risk to the Arctic environment. We propose a novel probabilistic method to quantitatively assess these risks. Our method accounts for spatiotemporally varying population distributions, the spreading of oil, and seasonally varying species-specific exposure potential and sensitivity to oil. It quantifies risk with explicit uncertainty estimates, enables one to compare risks over large geographic areas, and produces information on a meaningful scale for decision-making. We demonstrate the method by assessing the short-term risks oil spills pose to polar bears, ringed seals, and walrus in the Kara Sea, the western part of the Northern Sea Route. The risks differ considerably between species, spatial locations, and seasons. Our results support current aspirations to ban heavy fuel oil in the Arctic but show that we should not underestimate the risks of lighter oils either, as these oils can pollute larger areas than heavier ones. Our results also highlight the importance of spatially explicit season-specific oil spill risk assessment in the Arctic and that environmental variability and the lack of data are a major source of uncertainty related to the oil spill impacts.

Estimating the acute impacts of Arctic marine oil spills using expert elicitation.

Increasing maritime traffic in the Arctic has heightened the oil spill-related risks in this highly sensitive environment. To quantitatively assess these risks, we need knowledge about both the vulnerability and sensitivity of the key Arctic functional groups that may be affected by spilled oil. However, in the Arctic these data are typically scarce or lacking altogether. To compensate for this limited data availability, we propose the use of a probabilistic expert elicitation methodology, which we apply to seals, anatids, and seabirds. Our results suggest that the impacts of oil vary between functional groups, seasons, and oil types. Overall, the impacts are least for seals and greatest for anatids. Offspring seem to be more sensitive than adults, the impact is greatest in spring, and medium and heavy oils are the most harmful oil types. The elicitation process worked well, yet finding enough skilled and motivated experts proved to be difficult.

Preparing for the unprecedented - Towards quantitative oil risk assessment in the Arctic marine areas.

The probability of major oil accidents in Arctic seas is increasing alongside with increasing maritime traffic. Hence, there is a growing need to understand the risks posed by oil spills to these unique and sensitive areas. So far these risks have mainly been acknowledged in terms of qualitative descriptions. We introduce a probabilistic framework, based on a general food web approach, to analyze ecological impacts of oil spills. We argue that the food web approach based on key functional groups is more appropriate for providing holistic view of the involved risks than assessments based on single species. We discuss the issues characteristic to the Arctic that need a special attention in risk assessment, and provide examples how to proceed towards quantitative risk estimates. The conceptual model presented in the paper helps to identify the most important risk factors and can be used as a template for more detailed risk assessments.

A probabilistic approach for a cost-benefit analysis of oil spill management under uncertainty: A Bayesian network model for the Gulf of Finland.

Large-scale oil accidents can inflict substantial costs to the society, as they typically result in expensive oil combating and waste treatment operations and have negative impacts on recreational and environmental values. Cost-benefit analysis (CBA) offers a way to assess the economic efficiency of management measures capable of mitigating the adverse effects. However, the irregular occurrence of spills combined with uncertainties related to the possible effects makes the analysis a challenging task. We develop a probabilistic modeling approach for a CBA of oil spill management and apply it in the Gulf of Finland, the Baltic Sea. The model has a causal structure, and it covers a large number of factors relevant to the realistic description of oil spills, as well as the costs of oil combating operations at open sea, shoreline clean-up, and waste treatment activities. Further, to describe the effects on environmental benefits, we use data from a contingent valuation survey. The results encourage seeking for cost-effective preventive measures, and emphasize the importance of the inclusion of the costs related to waste treatment and environmental values in the analysis. Although the model is developed for a specific area, the methodology is applicable also to other areas facing the risk of oil spills as well as to other fields that need to cope with the challenging combination of low probabilities, high losses and major uncertainties.

Toward integrative management advice of water quality, oil spills, and fishery in the Gulf of Finland: a Bayesian approach.

Understanding and managing ecosystems affected by several anthropogenic stressors require methods that enable analyzing the joint effects of different factors in one framework. Further, as scientific knowledge about natural systems is loaded with uncertainty, it is essential that analyses are based on a probabilistic approach. We describe in this article about building a Bayesian decision model, which includes three stressors present in the Gulf of Finland. The outcome of the integrative model is a set of probability distributions for future nutrient concentrations, herring stock biomass, and achieving the water quality targets set by HELCOM Baltic Sea Action Plan. These distributions can then be used to derive the probability of reaching the management targets for each alternative combination of management actions.

Combining conservation value, vulnerability, and effectiveness of mitigation actions in spatial conservation decisions: an application to coastal oil spill combating.

Increasing oil transportation and severe oil accidents in the past have led to the development of various sensitivity maps in different countries all over the world. Often, however, the areas presented on the maps are far too large to be safeguarded with the available oil combating equipment and prioritization is required to decide which areas must be safeguarded. While oil booms can be applied to safeguard populations from a drifting oil slick, decision making on the spatial allocation of oil combating capacity is extremely difficult due to the lack of time, resources and knowledge. Since the operational decision makers usually are not ecologists, a useful decision support tool including ecological knowledge must be readily comprehensible and easy to use. We present an index-based method that can be used to make decisions concerning which populations of natural organisms should primarily be safeguarded from a floating oil slick with oil booms. The indices take into account the relative exposure, mortality and recovery potential of populations, the conservation value of species and populations, and the effectiveness of oil booms to safeguard different species. The method has been implemented in a mapping software that can be used in the Gulf of Finland (Baltic Sea) for operational oil combating. It could also be utilized in other similar conservation decisions where species with varying vulnerability, conservational value, and benefits received from the management actions need to be prioritized.

Modeling the effectiveness of oil combating from an ecological perspective--a Bayesian network for the Gulf of Finland; the Baltic Sea.

Maritime traffic poses a major threat to marine ecosystems in the form of oil spills. The Gulf of Finland, the easternmost part of the Baltic Sea, has witnessed a rapid increase in oil transportation during the last 15 years. Should a spill occur, the negative ecological impacts may be reduced by oil combating, the effectiveness of which is, however, strongly dependent on prevailing environmental conditions and available technical resources. This poses increased uncertainty related to ecological consequences of future spills. We developed a probabilistic Bayesian network model that can be used to assess the effectiveness of different oil combating strategies in minimizing the negative effects of oil on six species living in the Gulf of Finland. The model can be used for creating different accident scenarios and assessing the performance of various oil combating actions under uncertainty, which enables its use as a supportive tool in decision-making. While the model is confined to the western Gulf of Finland, the methodology is adaptable to other marine areas facing similar risks and challenges related to oil spills.